class: center, middle, inverse, title-slide .title[ # Hotspots del Vector del Dengue ] .author[ ### Felipe Antonio Dzul Manzanilla ] .date[ ### 2022: Last compiled 2022-10-27 ] --- class: left, top
### Ciclo de Vida del Programa de Prevención y Control del Dengue
--- ### Línea de Tiempo de la Vigilancia Epidemiológica & Entomológica <hr style="height:2px;border-width:0;color:#330019;background-color:#330019">
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--- ### **Análisis Espacial del Vector del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019">
--- ### **Análisis Espacial del Vector del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019">
.tiny[.blue[.footnote[ [Modificado de Zuur et al 2017](http://www.highstat.com/index.php/beginner-s-guide-to-regression-models-with-spatial-and-temporal-correlation); [Dzul-Manzanilla et al 2019](http://acaentmex.org/entomologia/revista/2019/EMF/EMF%20497-501.pdf)]]] --- ### **Análisis Espacial del Vector del Dengue** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019">
.tiny[.blue[.footnote[ [Modificado de Zuur et al 2017](http://www.highstat.com/index.php/beginner-s-guide-to-regression-models-with-spatial-and-temporal-correlation); [Dzul-Manzanilla et al 2019](http://acaentmex.org/entomologia/revista/2019/EMF/EMF%20497-501.pdf)]]] --- ### **Vigilancia Entomológica con Ovitrampas** <hr style="height:2px;border-width:0;color:#330019;background-color:#330019"> ```r #step 1. load the ovitramps load("C:/Users/HOME/Dropbox/ovitraps_activas&casesmx_klm/8.RData/ovitraps_coordinates.RData") # Step 3. load the localities loc_urb <- rgeomex::loc_inegi19_mx |> sf::st_make_valid() # Step 4. extract the localities with ovitraps loc_urb_ovi <- loc_urb[x,] # Step 5. load the high risk localities y <- readRDS("C:/Users/HOME/OneDrive/proyects/hotspots_paper/8.RData/loc_temp_geocoded.rds") # Step 6. convert th df to sf #### y <- y |> sf::st_as_sf(coords = c("long", "lat"), crs = 4326) # Step 7. extract the localities with high risk with ovitraps #### loc_urb_ovi_hr <- loc_urb_ovi[y, ] # Step 8. load several sf object for maps #### mex_edo <- rgeomex::AGEE_inegi19_mx path <- "C:/Users/HOME/OneDrive/datasets/INEGI/mapa_digital_5.0.A/marco geoestadistico nacional 2010/nacional.shp" mex <- sf::st_read(path, quiet = TRUE) |> sf::st_transform(crs = 4326) mapview::mapview(mex_edo, legend = FALSE, color = "white", #col.regions = "white", lwd = 3, alpha.regions = 0.1, alpha = 0.1) + mapview::mapview(loc_urb_ovi, layer.name = "Ovitrampas", col.regions = "green", color = "green") + mapview::mapview(loc_urb_ovi_hr, layer.name = "Ovitrampas Localidades Alto Riesgo", col.regions = "red", color = "red") ```